%0 Journal Article
%A Shahsavani, Narjes
%A Sheikhha, Mohammad Hasan
%A Yousefi, Hasan
%A Sefid, Fatemeh
%T In silico Homology Modeling and Epitope Prediction of NadA as a Potential Vaccine Candidate in Neisseria meningitidis
%J International Journal of Molecular and Cellular Medicine
%V 7
%N 1
%U http://ijmcmed.org/article-1-708-en.html
%R 10.22088/IJMCM.BUMS.7.1.53
%D 2018
%K Neisseria meningitidis, NadA, epitope prediction, 3D structure,
%X Neisseria meningitidis is a facultative pathogen bacterium which is well founded with a number of adhesion molecules to facilitate its colonization in human nasopharynx track. Neisseria meningitidis is a major cause of mortality from sever meningococcal disease and septicemia. The Neisseria meningitidis adhesion, NadA, is a trimeric autotransporter adhesion molecule which is involved in cell adhesion, invasion, and antibody induction. It is identified in approximately 50% of N. meningitidis isolates, and is established as a vaccine candidate due to its antigenic effects. In the present study we exploited bioinformatics tools to better understand and determine the 3D structure of NadA and its functional residues to select B cell epitopes, and provide information for elucidating the biological function and vaccine efficacy of NadA. Therefore, this study provided essential data to close gaps existing in biological areas. The most appropriate model of NadA was designed by SWISS MODEL software and important residues were determined using the subsequent epitope mapping procedures. Locations of important linear and conformational epitopes were determined and conserved residues were identified to broaden our knowledge of efficient vaccine designing to reduce meningococcal infectious in population. These data now provide a theme to design more broadly cross-protective antigens.
%> http://ijmcmed.org/article-1-708-en.pdf
%P 53-68
%& 53
%! In silico NadA Epitope Prediction in N. meningitidis
%9 Original Article
%L A-10-1207-1
%+ Department of Biology, Science and Art University, Yazd, Iran.
%G eng
%@ 2251-9637
%[ 2018